Breath analysis for in vivo detection of pathogens related to ventilator-associated pneumonia in intensive care patients: a prospective pilot study

Exhaled Volatile Organic Compounds Detection in Pneumonia Screening: A Comprehensive Meta-analysis

BACKGROUND

Pneumonia is a common lower respiratory tract infection, and early diagnosis is crucial for timely treatment and improved prognosis. Traditional diagnostic methods for pneumonia, such as chest imaging and microbiological examinations, have certain limitations. Exhaled volatile organic compounds (VOCs) detection, as an emerging non-invasive diagnostic technique, has shown potential application value in pneumonia screening.

METHOD

A systematic search was conducted on PubMed, Embase, Cochrane Library, and Web of Science, with the retrieval time up to March 2024. The inclusion criteria were diagnostic studies evaluating exhaled VOCs detection for the diagnosis of pneumonia, regardless of the trial design type. A meta-analysis was performed using a bivariate model for sensitivity and specificity.

RESULTS

A total of 14 diagnostic studies were included in this meta-analysis. The pooled results demonstrated that exhaled VOCs detection had a combined sensitivity of 0.94 (95% CI: 0.92-0.95) and a combined specificity of 0.83 (95% CI: 0.81-0.84) in pneumonia screening, with an area under the summary receiver operating characteristic (SROC) curve (AUC) of 0.96. The pooled diagnostic odds ratio (DOR) was 104.37 (95% CI: 27.93-390.03), and the pooled positive and negative likelihood ratios (LR) were 8.98 (95% CI: 3.88-20.80) and 0.11 (95% CI: 0.05-0.22), indicating a high diagnostic performance.

CONCLUSION

This study highlights the potential of exhaled VOCs detection as an effective, non-invasive screening method for pneumonia, which could facilitate future diagnosis in pneumonia. Further high-quality, large-scale studies are required to confirm the clinical utility of exhaled VOCs detection in pneumonia screening.

STUDY REGISTRATION

PROSPERO, Review no. CRD42024520498.

Molecular Analysis of Volatile Metabolites Synthesized by Candida albicans and Staphylococcus aureus in In Vitro Cultures and Bronchoalveolar Lavage Specimens Reflecting Single- or Duo-Factor Pneumonia

Current microbiological methods for pneumonia diagnosis require invasive specimen collection and time-consuming analytical procedures. There is a need for less invasive and faster methods to detect lower respiratory tract infections. The analysis of volatile metabolites excreted by pathogenic microorganisms provides the basis for developing such a method. Given the synergistic role of Candida albicans in increasing the virulence of pathogenic bacteria causing pneumonia and the cross-kingdom metabolic interactions between microorganisms, we compare the emission of volatiles from Candida albicans yeasts and the bacteria Staphylococcus aureus using single and mixed co-cultures and apply that knowledge to human in vivo investigations. Gas chromatography-mass spectrometry (GC-MS) analysis resulted in the identification of sixty-eight volatiles that were found to have significantly different levels in cultures compared to reference medium samples. Certain volatiles were found in co-cultures that mainly originated from C. albicans metabolism (e.g., isobutyl acetate), whereas other volatiles primarily came from S. aureus (e.g., ethyl 2-methylbutyrate). Isopentyl valerate reflects synergic interactions of both microbes, as its level in co-cultures was found to be approximately three times higher than the sum of its amounts in monocultures. Hydrophilic-lipophilic-balanced (HLB) coated meshes for thin-film microextraction (TFME) were used to preconcentrate volatiles directly from bronchoalveolar lavage (BAL) specimens collected from patients suffering from ventilation-associated pneumonia (VAP), which was caused explicitly by C. albicans and S. aureus. GC-MS analyses confirmed the existence of in vitro-elucidated microbial VOCs in human specimens. Significant differences in BAL-extracted amounts respective to the pathogen-causing pneumonia were found. The model in vitro experiments provided evidence that cross-kingdom interactions between pathogenic microorganisms affect the synthesis of volatile compounds. The TFME meshes coated with HLB particles proved to be suitable for extracting VOCs from human material, enabling the translation of in vitro experiments on the microbial volatilome to the in vivo situation involving infected patients. This indicates the direction that should be taken for further clinical studies on VAP diagnosis based on volatile analysis.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria: Current state of the art

Pathogenic microorganisms produce numerous metabolites, including volatile organic compounds (VOCs). Monitoring these metabolites in biological matrices (e.g., urine, blood, or breath) can reveal the presence of specific microorganisms, enabling the early diagnosis of infections and the timely implementation of targeted therapy. However, complex matrices only contain trace levels of VOCs, and their constituent components can hinder determination of these compounds. Therefore, modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed. In this paper, we discuss bacterial VOC analysis under in vitro conditions, in animal models and disease diagnosis in humans, including techniques for offline and online analysis in clinical settings. We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis, in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species interactions, the kinetics of VOC metabolism, and species- and drug-resistance specificity.

Dogs can detect an odor profile associated with Staphylococcus aureus biofilms in cultures and biological samples

Introduction

The study investigated the utilization of odor detection dogs to identify the odor profile of Staphylococcus aureus (S. aureus) biofilms in pure in vitro samples and in in vivo biosamples from animals and humans with S. aureus periprosthetic joint infection (PJI). Biofilms form when bacterial communities aggregate on orthopedic implants leading to recalcitrant infections that are difficult to treat. Identifying PJI biofilm infections is challenging, and traditional microbiological cultures may yield negative results even in the presence of clinical signs.

Methods

Dogs were trained on pure in vitro S. aureus biofilms and tested on lacrimal fluid samples from an in vivo animal model (rabbits) and human patients with confirmed S. aureus PJI.

Results

The results demonstrated that dogs achieved a high degree of sensitivity and specificity in detecting the odor profile associated with S. aureus biofilms in rabbit samples. Preliminary results suggest that dogs can recognize S. aureus volatile organic compounds (VOCs) in human lacrimal fluid samples.

Discussion

Training odor detection dogs on in vitro S. aureus, may provide an alternative to obtaining clinical samples for training and mitigates biosecurity hazards. The findings hold promise for culture-independent diagnostics, enabling early disease detection, and improved antimicrobial stewardship. In conclusion, this research demonstrates that dogs trained on in vitro S. aureus samples can identify the consistent VOC profile of PJI S. aureus biofilm infections. The study opens avenues for further investigations into a retained VOC profile of S. aureus biofilm infection. These advancements could revolutionize infectious disease diagnosis and treatment, leading to better patient outcomes and addressing the global challenge of antimicrobial resistance.

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity

INTRODUCTION

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media.

METHODS

Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation.

RESULTS

The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature.

CONCLUSION

This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Volatilomes of human infection

The human volatilome comprises a vast mixture of volatile emissions produced by the human body and its microbiomes. Following infection, the human volatilome undergoes significant shifts, and presents a unique medium for non-invasive biomarker discovery. In this review, we examine how the onset of infection impacts the production of volatile metabolites that reflects dysbiosis by pathogenic microbes. We describe key analytical workflows applied across both microbial and clinical volatilomics and emphasize the value in linking microbial studies to clinical investigations to robustly elucidate the metabolic species and pathways leading to the observed volatile signatures. We review the current state of the art across microbial and clinical volatilomics, outlining common objectives and successes of microbial-clinical volatilomic workflows. Finally, we propose key challenges, as well as our perspectives on emerging opportunities for developing clinically useful and targeted workflows that could significantly enhance and expedite current practices in infection diagnosis and monitoring.

Ampelopsin attenuates Staphylococcus aureus Alpha-Toxin-Induced Lung Injury

Staphylococcus aureus is a prevalent cause of lung infections in hospitals and communities, and can cause a wide spectrum of human infections. Due to the bottleneck caused by antibiotic resistance and substantial increases in morbidity and mortality, targeting the virulence factors released by S. aureus as an alternative prevention and treatment method has become a promising approach. Ampelopsin, a component of vine tea, has promising potential for treating S. aureus-induced acute lung injury. In this study, the effects of ampelopsin were investigated on a mouse model of acute lung injury established using S. aureus 8325-4 and the α-hemolysin (hla) silent strain DU1090. The hla silent strain did not cause mortality in mice, whereas lethal and sublethal concentrations of S. aureus 8325-4 caused high mortality. Notably, ampelopsin treatment protected against mortality stemming from S. aureus infection. Ampelopsin yielded enhancements in lung barrier function, decreased total protein leakage in the alveolar lavage fluid, and modulated inflammatory signaling pathway-related proteins, thereby reducing the release of pro-inflammatory factors and improving respiratory dysfunction. Moreover, ampelopsin prevented the upregulation of ADAM10 activity, leading to E-cadherin mucin cleavage. In conclusion, our findings establish the key role of alpha -toxin in infectious lung injury in S. aureus and provide support for ampelopsin as an effective therapeutic approach to improve lung injury.

Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

Influence of bacterial and alveolar cell co-culture on microbial VOC production using HS-GC/MS

Volatile organic compounds (VOCs) found in exhaled breath continue to garner interest as an alternative diagnostic tool in pulmonary infections yet, their clinical integration remains a challenge with difficulties in translating identified biomarkers. Alterations in bacterial metabolism secondary to host nutritional availability may explain this but is often inadequately modelled in vitro. The influence of more clinically relevant nutrients on VOC production for two common respiratory pathogens was investigated. VOCs from Staphylococcus aureus (S.aureus) and Pseudomonas aeruginosa (P.aeruginosa) cultured with and without human alveolar A549 epithelial cells were analyzed using headspace extraction coupled with gas chromatography-mass spectrometry. Untargeted and targeted analyses were performed, volatile molecules identified from published data, and the differences in VOC production evaluated. Principal component analysis (PCA) could differentiate alveolar cells from either S. aureus or P. aeruginosa when cultured in isolation based on PC1 (p = 0.0017 and 0.0498, respectively). However, this separation was lost for S. aureus (p = 0.31) but not for P. aeruginosa (p = 0.028) when they were cultured with alveolar cells. S. aureus cultured with alveolar cells led to higher concentrations of two candidate biomarkers, 3-methyl-1-butanol (p = 0.001) and 3-methylbutanal (p = 0.002) when compared to S. aureus, alone. P. aeruginosa metabolism resulted in less generation of pathogen-associated VOCs when co-cultured with alveolar cells compared to culturing in isolation. VOC biomarkers previously considered indicative of bacterial presence are influenced by the local nutritional environment and this should be considered when evaluating their biochemical origin.

Microbial Volatiles as Diagnostic Biomarkers of Bacterial Lung Infection in Mechanically Ventilated Patients

BACKGROUND

Early and accurate recognition of respiratory pathogens is crucial to prevent increased risk of mortality in critically ill patients. Microbial-derived volatile organic compounds (mVOCs) in exhaled breath could be used as noninvasive biomarkers of infection to support clinical diagnosis.

METHODS

In this study, we investigated the diagnostic potential of in vitro-confirmed mVOCs in the exhaled breath of patients under mechanical ventilation from the BreathDx study. Samples were analyzed by thermal desorption-gas chromatography-mass spectrometry.

RESULTS

Pathogens from bronchoalveolar lavage (BAL) cultures were identified in 45 of 89 patients and Staphylococcus aureus was the most commonly identified pathogen (n = 15). Of 19 mVOCs detected in the in vitro culture headspace of 4 common respiratory pathogens (S. aureus, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli), 14 were found in exhaled breath samples. Higher concentrations of 2 mVOCs were found in the exhaled breath of patients infected with S. aureus compared to those without (3-methylbutanal: P < .01, area under the receiver operating characteristic curve [AUROC] = 0.81-0.87; and 3-methylbutanoic acid: P = .01, AUROC = 0.79-0.80). In addition, bacteria identified from BAL cultures that are known to metabolize tryptophan (E. coli, Klebsiella oxytoca, and Haemophilus influenzae) were grouped and found to produce higher concentrations of indole compared to breath samples with culture-negative (P = .034) and other pathogen-positive (P = .049) samples.

CONCLUSIONS

This study demonstrates the capability of using mVOCs to detect the presence of specific pathogen groups with potential to support clinical diagnosis. Although not all mVOCs were found in patient samples within this small pilot study, further targeted and qualitative investigation is warranted using multicenter clinical studies.

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study

Background

Changes in exhaled volatile organic compounds (VOCs) can be used to discriminate between respiratory diseases, and increased concentrations of hydrocarbons are commonly linked to oxidative stress. However, the VOCs identified are inconsistent between studies, and translational studies are lacking.

Methods

In this bench to bedside study, we captured VOCs in the headspace of A549 epithelial cells after exposure to hydrogen peroxide (H₂O₂), to induce oxidative stress, using high-capacity polydimethylsiloxane sorbent fibres. Exposed and unexposed cells were compared using targeted and untargeted analysis. Breath samples of invasively ventilated intensive care unit patients (n=489) were collected on sorbent tubes and associated with the inspiratory oxygen fraction (F _IO2 ) to reflect pulmonary oxidative stress. Headspace samples and breath samples were analysed using gas chromatography and mass spectrometry.

Results

In the cell, headspace octane concentration was decreased after oxidative stress (p=0.0013), while the other VOCs were not affected. 2-ethyl-1-hexanol showed an increased concentration in the headspace of cells undergoing oxidative stress in untargeted analysis (p=0.00014). None of the VOCs that were linked to oxidative stress showed a significant correlation with F _IO2 (R_s range: -0.015 to -0.065) or discriminated between patients with F _IO2 ≥0.6 or below (area under the curve range: 0.48 to 0.55).

Conclusion

Despite a comprehensive translational approach, validation of known and novel volatile biomarkers of oxidative stress was not possible in patients at risk of pulmonary oxidative injury. The inconsistencies observed highlight the difficulties faced in VOC biomarker validation, and that caution is warranted in the interpretation of the pathophysiological origin of discovered exhaled breath biomarkers.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Volatile metabolites differentiate air-liquid interface cultures after infection with Staphylococcus aureus

Early detection of lung infection is critical to clinical diagnosis, treatment, and monitoring. Measuring volatile organic compounds (VOCs) in exhaled breath has shown promise as a rapid and accurate method of evaluating disease metabolism and phenotype. However, further investigations of the role and function of VOCs in bacterial-host-stress response is required and this can only be realised through representative in vitro models. In this study we sampled VOCs from the headspace of A549 cells at an air-liquid interface (ALI). We hypothesised VOC sampling from ALI cultures could be used to profile potential biomarkers of S. aureus lung infection. VOCs were collected using thin film microextraction (TFME) and were analysed by thermal desorption-gas chromatography-mass spectrometry. After optimising ALI cultures, we observed seven VOCs changed between A549 and media control samples. After infecting cells with S. aureus, supervised principal component-discriminant function analysis revealed 22 VOCs were found to be significantly changed in infected cells compared to uninfected cells (p < 0.05), five of which were also found in parallel axenic S. aureus cultures. We have demonstrated VOCs that could be used to identify S. aureus in ALI cultures, supporting further investigation of VOC analysis as a highly sensitive and specific test for S. aureus lung infection.

Current Limitations of Staph Infection Diagnostics, and the Role for VOCs in Achieving Culture-Independent Detection

Staphylococci are broadly adaptable and their ability to grow in unique environments has been widely established, but the most common and clinically relevant staphylococcal niche is the skin and mucous membranes of mammals and birds. S. aureus causes severe infections in mammalian tissues and organs, with high morbidities, mortalities, and treatment costs. S. epidermidis is an important human commensal but is also capable of deadly infections. Gold-standard diagnostic methods for staph infections currently rely upon retrieval and characterization of the infectious agent through various culture-based methods. Yet, obtaining a viable bacterial sample for in vitro identification of infection etiology remains a significant barrier in clinical diagnostics. The development of volatile organic compound (VOC) profiles for the detection and identification of pathogens is an area of intensive research, with significant efforts toward establishing breath tests for infections. This review describes the limitations of existing infection diagnostics, reviews the principles and advantages of VOC-based diagnostics, summarizes the analytical tools for VOC discovery and clinical detection, and highlights examples of how VOC biomarkers have been applied to diagnosing human and animal staph infections.

Volatile Metabolites in Lavage Fluid Are Correlated with Cytokine Production in a Valley Fever Murine Model

Coccidioides immitis and Coccidioides posadasii are soil-dwelling fungi of arid regions in North and South America that are responsible for Valley fever (coccidioidomycosis). Forty percent of patients with Valley fever exhibit symptoms ranging from mild, self-limiting respiratory infections to severe, life-threatening pneumonia that requires treatment. Misdiagnosis as bacterial pneumonia commonly occurs in symptomatic Valley fever cases, resulting in inappropriate treatment with antibiotics, increased medical costs, and delay in diagnosis. In this proof-of-concept study, we explored the feasibility of developing breath-based diagnostics for Valley fever using a murine lung infection model. To investigate potential volatile biomarkers of Valley fever that arise from host−pathogen interactions, we infected C57BL/6J mice with C. immitis RS (n = 6), C. posadasii Silveira (n = 6), or phosphate-buffered saline (n = 4) via intranasal inoculation. We measured fungal dissemination and collected bronchoalveolar lavage fluid (BALF) for cytokine profiling and for untargeted volatile metabolomics via solid-phase microextraction (SPME) and two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). We identified 36 volatile organic compounds (VOCs) that were significantly correlated (p < 0.05) with cytokine abundance. These 36 VOCs clustered mice by their cytokine production and were also able to separate mice with moderate-to-high cytokine production by infection strain. The data presented here show that Coccidioides and/or the host produce volatile metabolites that may yield biomarkers for a Valley fever breath test that can detect coccidioidal infection and provide clinically relevant information on primary pulmonary disease severity.

GC-MS profiling of volatile metabolites produced by Klebsiella pneumoniae

Currently used methods for diagnosing ventilator-associated pneumonia (VAP) are complex, time-consuming and require invasive procedures while empirical antibacterial therapy applies broad spectrum antibiotics that may promote antimicrobial resistance. Hence, novel and fast methods based on alternative markers are needed for VAP detection and differentiation of causative pathogens. Pathogenic bacteria produce a broad range of volatile organic compounds (VOCs), some of which may potentially serve as biomarkers for microorganism identification. Additionally, monitoring of dynamically changing VOCs concentration profiles may indicate emerging pneumonia and allow timely implementation of appropriate antimicrobial treatment. This study substantially extends the knowledge on bacterial metabolites providing the unambiguous identification of volatile metabolites produced by carbapenem-resistant and susceptible strains of Klebsiella pneumoniae (confirmed with pure standards in addition to mass spectra match) but also revealing their temporary concentration profiles (along the course of pathogen proliferation) and dependence on the addition of antibiotic (imipenem) to bacteria. Furthermore, the clinical strains of K. pneumoniae isolated from bronchoalveolar lavage specimens collected from mechanically ventilated patients were investigated to reveal, whether bacterial metabolites observed in model experiments with reference strains could be relevant for wild pathogens as well. In all experiments, the headspace samples from bacteria cultures were collected on multibed sorption tubes and analyzed by GC-MS. Sampling was done under strictly controlled conditions at seven time points (up to 24 h after bacteria inoculation) to follow the dynamic changes in VOC concentrations, revealing three profiles: release proportional to bacteria load, temporary maximum and uptake. Altogether 32 VOCs were released by susceptible and 25 VOCs by resistant strain, amongst which 2-pentanone, 2-heptanone, and 2-nonanone were significantly higher for carbapenem-resistant KPN. Considerably more metabolites (n = 64) were produced by clinical isolates and in higher diversity compared to reference KPN strains.

The potential of volatile organic compound analysis for pathogen detection and disease monitoring in patients with cystic fibrosis

INTRODUCTION

Airway infection with pathogens and its associated pulmonary exacerbations (PEX) are the major causes of morbidity and premature death in cystic fibrosis (CF). Preventing or postponing chronic infections requires early diagnosis. However, limitations of conventional microbiology-based methods can hamper identification of exacerbations and specific pathogen detection. Analyzing volatile organic compounds (VOCs) in breath samples may be an interesting tool in this regard, as VOC-biomarkers can characterize specific airway infections in CF.

AREAS COVERED

We address the current achievements in VOC-analysis and discuss studies assessing VOC-biomarkers and fingerprints, i.e. a combination of multiple VOCs, in breath samples aiming at pathogen and PEX detection in people with CF (pwCF). We aim to provide bases for further research in this interesting field.

EXPERT OPINION

Overall, VOC-based analysis is a promising tool for diagnosis of infection and inflammation with potential to monitor disease progression in pwCF. Advantages over conventional diagnostic methods, including easy and non-invasive sampling procedures, may help to drive prompt, suitable therapeutic approaches in the future. Our review shall encourage further research, including validation of VOC-based methods. Specifically, longitudinal validation under standardized conditions is of interest in order to ensure repeatability and enable inclusion in CF diagnostic routine.

Metabolomics Diagnosis of COVID-19 from Exhaled Breath Condensate

Infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and acute lung injury. Therefore, it is crucial to study breath-associated biofluids not only to investigate the breath's biochemical changes caused by SARS-CoV-2 infection, but also to discover potential biomarkers for the development of new diagnostic tools. In the present study, we performed an untargeted metabolomics approach using a bidimensional gas chromatography mass spectrometer (GCxGC-TOFMS) on exhaled breath condensate (EBC) from COVID-19 patients and negative healthy subjects to identify new potential biomarkers for the noninvasive diagnosis and monitoring of the COVID-19 disease. The EBC analysis was further performed in patients with acute or acute-on-chronic cardiopulmonary edema (CPE) to assess the reliability of the identified biomarkers. Our findings demonstrated that an abundance of EBC fatty acids can be used to discriminate COVID-19 patients and that they may have a protective effect, thus suggesting their potential use as a preventive strategy against the infection.

Breath and plasma metabolomics to assess inflammation in acute stroke

Inflammation is strongly implicated in both injury and repair processes occurring after stroke. In this exploratory study we assessed the feasibility of repeated sampling of exhaled volatile organic compounds and performed an untargeted metabolomic analysis of plasma collected at multiple time periods after stroke. Metabolic profiles were compared with the time course of the inflammatory markers C-reactive protein (CRP) and interleukin-6 (IL-6). Serial breath sampling was well-tolerated by all patients and the measurement appears feasible in this group. We found that exhaled decanal tracks CRP and IL-6 levels post-stroke and correlates with several metabolic pathways associated with a post-stroke inflammatory response. This suggests that measurement of breath and blood metabolites could facilitate development of novel therapeutic and diagnostic strategies. Results are discussed in relation to the utility of breath analysis in stroke care, such as in monitoring recovery and complications including stroke associated infection.

An Investigation of Stability and Species and Strain-Level Specificity in Bacterial Volatilomes

Microbial volatilomics is a rapidly growing field of study and has shown great potential for applications in food, farming, and clinical sectors in the future. Due to the varying experimental methods and growth conditions employed in microbial volatilomic studies as well as strain-dependent volatilomic differences, there is limited knowledge regarding the stability of microbial volatilomes. Consequently, cross-study comparisons and validation of results and data can be challenging. In this study, we investigated the stability of the volatilomes of multiple strains of Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli across three frequently used nutrient-rich growth media. Volatilomic stability was assessed based on media-, time- and strain-dependent variation across the examined bacterial volatilomes. Strain-level specificity of the observed volatilomes of E. coli and P. aeruginosa strains was further investigated by comparing the emission of selected compounds at varying stages of cell growth. Headspace solid phase microextraction (HS-SPME) sampling coupled with gas chromatography mass spectrometry (GC-MS) was used to analyze the volatilome of each strain. The whole volatilomes of the examined strains demonstrate a high degree of stability across the three examined growth media. At the compound-level, media dependent differences were observed particularly when comparing the volatilomes obtained in glucose-containing brain heart infusion (BHI) and tryptone soy broth (TSB) growth media with the volatilomes obtained in glucose-free Lysogeny broth (LB) media. These glucose-dependent volatilomic differences were primarily seen in the emission of primary metabolites such as alcohols, ketones, and acids. Strain-level differences in the emission of specific compounds in E. coli and P. aeruginosa samples were also observed across the media. These strain-level volatilomic differences were also observed across varying phases of growth of each strain, therefore confirming that these strains had varying core and accessory volatilomes. Our results demonstrate that, at the species-level, the examined bacteria have a core volatilome that exhibits a high-degree of stability across frequently-used growth media. Media-dependent differences in microbial volatilomes offer valuable insights into identifying the cellular origin of individual metabolites. The observed differences in the core and accessory volatilomes of the examined strains illustrate the complexity of microbial volatilomics as a study while also highlighting the need for more strain-level investigations to ultimately elucidate the whole volatilomic capabilities of microbial species in the future.

Reproducible Breath Metabolite Changes in Children with SARS-CoV-2 Infection

SARS-CoV-2 infection is diagnosed through detection of specific viral nucleic acid or antigens from respiratory samples. These techniques are relatively expensive, slow, and susceptible to false-negative results. A rapid noninvasive method to detect infection would be highly advantageous. Compelling evidence from canine biosensors and studies of adults with COVID-19 suggests that infection reproducibly alters human volatile organic compound (VOC) profiles. To determine whether pediatric infection is associated with VOC changes, we enrolled SARS-CoV-2 infected and uninfected children admitted to a major pediatric academic medical center. Breath samples were collected from children and analyzed through state-of-the-art GCxGC-ToFMS. Isolated features included 84 targeted VOCs. Candidate biomarkers that were correlated with infection status were subsequently validated in a second, independent cohort of children. We thus find that six volatile organic compounds are significantly and reproducibly increased in the breath of SARS-CoV-2 infected children. Three aldehydes (octanal, nonanal, and heptanal) drew special attention, as aldehydes are also elevated in the breath of adults with COVID-19. Together, these biomarkers demonstrate high accuracy for distinguishing pediatric SARS-CoV-2 infection and support the ongoing development of novel breath-based diagnostics.

Identification of volatile compounds from bacteria by spectrometric methods in medicine diagnostic and other areas: current state and perspectives

Diagnosis of bacterial infections until today mostly relies on conventional microbiological methods. The resulting long turnaround times can lead to delayed initiation of adequate antibiotic therapy and prolonged periods of empiric antibiotic therapy (e.g., in intensive care medicine). Therewith, they contribute to the mortality of bacterial infections and the induction of multidrug resistances. The detection of species specific volatile organic compounds (VOCs) emitted by bacteria has been proposed as a possible diagnostic approach with the potential to serve as an innovative point-of-care diagnostic tool with very short turnaround times. A range of spectrometric methods are available which allow the detection and quantification of bacterial VOCs down to a range of part per trillion. This narrative review introduces the application of spectrometric analytical methods for the purpose of detecting VOCs of bacterial origin and their clinical use for diagnosing different infectious conditions over the last decade. KEY POINTS: • Detection of VOCs enables bacterial differentiation in various medical conditions. • Spectrometric methods may function as point-of-care diagnostics in near future.

Volatile Organic Compounds to Identify Infectious (Bacteria/Viruses) Diseases of the Central Nervous System: A Pilot Study

BACKGROUND

Central nervous system (CNS) infectious diseases are common diseases in emergency rooms and neurology departments. CNS pathogen identification methods are time consuming and expensive and have low sensitivity and poor specificity. Some studies have shown that bacteria and viruses can produce specific volatile organic compounds (VOCs). The aim of this study is to find potential biomarkers by VOC analysis of cerebrospinal fluid (CSF) in patients with bacterial and viral meningitis/encephalitis (ME).

METHODS

CSF samples from 16 patients with bacterial ME and 42 patients with viral ME were collected, and solid-phase microextraction combined with gas chromatography-mass spectrometry was used to analyze the metabolites in the CSF.

RESULTS

There are 2 substances (ethylene oxide and phenol) that were found to be different between the 2 groups. Ethylene oxide was significantly greater in the group of bacterial ME patients than in the viral ME group of patients (p < 0.05). In addition, phenol was remarkably increased in the group of ME patients compared with the bacterial ME patients (p < 0.05).

CONCLUSIONS

Ethylene oxide and phenol may be potential biomarkers to distinguish bacterial ME and viral ME. VOC analysis of CSF may be used as a supporting tool for clinical diagnosis.

Breath-Based Diagnosis of Infectious Diseases: A Review of the Current Landscape

Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.

Reproducible breath metabolite changes in children with SARS-CoV-2 infection

SARS-CoV-2 infection is diagnosed through detection of specific viral nucleic acid or antigens from respiratory samples. These techniques are relatively expensive, slow, and susceptible to false-negative results. A rapid non-invasive method to detect infection would be highly advantageous. Compelling evidence from canine biosensors and studies of adults with COVID-19 suggests that infection reproducibly alters human volatile organic compounds (VOCs) profiles. To determine whether pediatric infection is associated with VOC changes, we enrolled SARS-CoV-2-infected and -uninfected children admitted to a major pediatric academic medical center. Breath samples were collected from children and analyzed through state-of-the-art GCxGC-ToFMS. Isolated features included 84 targeted VOCs. Candidate biomarkers that were correlated with infection status were subsequently validated in a second, independent cohort of children. We thus find that six volatile organic compounds are significantly and reproducibly increased in the breath of SARS-CoV-2-infected children. Three aldehydes (octanal, nonanal, and heptanal) drew special attention, as aldehydes are also elevated in the breath of adults with COVID-19. Together, these biomarkers demonstrate high accuracy for distinguishing pediatric SARS-CoV-2 infection and support the ongoing development of novel breath-based diagnostics.

Life Cycle Dominates the Volatilome Character of Dimorphic Fungus Coccidioides spp

Valley fever (coccidioidomycosis) is an endemic fungal pneumonia of the North and South American deserts. The causative agents of Valley fever are the dimorphic fungi Coccidioides immitis and C. posadasii, which grow as mycelia in the environment and as spherules within the lungs of vulnerable hosts. Current diagnostics for Valley fever are severely lacking due to poor sensitivity and invasiveness, contributing to a 23-day median time to diagnosis, and therefore, new diagnostic tools are needed. We are working toward the development of a breath-based diagnostic for coccidioidomycosis, and in this initial study, we characterized the volatile metabolomes (or volatilomes) of in vitro cultures of Coccidioides. Using solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry (GC×GC-TOFMS), we characterized the volatile organic compounds (VOCs) produced by six strains of each species during mycelial or spherule growth. We detected a total of 353 VOCs that were at least 2-fold more abundant in a Coccidioides culture than in medium controls and found that the volatile metabolome of Coccidioides is more dependent on the growth phase (spherules versus mycelia) than on the species. The volatile profiles of C. immitis and C. posadasii have strong similarities, indicating that a single suite of Valley fever breath biomarkers can be developed to detect both species.

IMPORTANCE Coccidioidomycosis, or Valley fever, causes up to 30% of community-acquired pneumonias in highly populated areas of the U.S. desert southwest where the disease is endemic. The infection is difficult to diagnose by standard serological and histopathological methods, which delays appropriate treatment. Therefore, we are working toward the development of breath-based diagnostics for Valley fever. In this study, we characterized the volatile metabolomes (or volatilomes) of six strains each of Coccidioides immitis and C. posadasii, the dimorphic fungal species that cause Valley fever. By analyzing the volatilomes during the two modes of growth of the fungus—mycelia and spherules—we observed that the life cycle plays a significant role in the volatiles produced by Coccidioides. In contrast, we observed no significant differences in the C. immitis versus C. posadasii volatilomes. These data suggest that life cycle, rather than species, should guide the selection of putative biomarkers for a Valley fever breath test.

Nosocomial Pneumonia in the Era of Multidrug-Resistance: Updates in Diagnosis and Management

Nosocomial pneumonia (NP), including hospital-acquired pneumonia in non-intubated patients and ventilator-associated pneumonia, is one of the most frequent hospital-acquired infections, especially in the intensive care unit. NP has a significant impact on morbidity, mortality and health care costs, especially when the implicated pathogens are multidrug-resistant ones. This narrative review aims to critically review what is new in the field of NP, specifically, diagnosis and antibiotic treatment. Regarding novel imaging modalities, the current role of lung ultrasound and low radiation computed tomography are discussed, while regarding etiological diagnosis, recent developments in rapid microbiological confirmation, such as syndromic rapid multiplex Polymerase Chain Reaction panels are presented and compared with conventional cultures. Additionally, the volatile compounds/electronic nose, a promising diagnostic tool for the future is briefly presented. With respect to NP management, antibiotics approved for the indication of NP during the last decade are discussed, namely, ceftobiprole medocaril, telavancin, ceftolozane/tazobactam, ceftazidime/avibactam, and meropenem/vaborbactam.

Advances in molecular diagnostic tests for pneumonia

PURPOSE OF REVIEW

Pneumonia is the main global cause of sepsis, and has been associated with high morbidity and high short and long-term mortality rates. As it may be caused by a wide spectrum of microorganisms, microbial diagnosis is challenging and the choice of adequate therapy remains an important problem. This review focuses on recently published studies of microbiological diagnostic tests and clinical assessments for pneumonia, including community-acquired pneumonia, hospital-acquired pneumonia, and ventilator-associated pneumonia.

RECENT FINDINGS

Over the past decade, the microbiological diagnosis of pneumonia has improved significantly - thanks to the development and implementation of molecular diagnostic tests for identifying the most frequent pathogens causing pneumonia and for determining their patterns of resistance. Molecular methods for the diagnosis of pneumonia focus on multiple target detection systems and pathogen detection arrays, and, more recently, have been used in combination with mass spectrometry.

SUMMARY

The implementation of rapid diagnostic techniques in routine clinical practice able to identify and determine the resistance patterns of the causative microbes may transform the management of pneumonia, improving the selection and administration of antimicrobial therapies especially in critically ill patients. The validation of new diagnostic technology platforms is crucial in order to assess their usefulness and to guide antimicrobial treatment in this population.

Breath profile as composite biomarkers for lung cancer diagnosis

OBJECTIVES

Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality.

METHODS

An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods.

RESULTS

An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations.

CONCLUSION

We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected.

Detection and quantification of exhaled volatile organic compounds in mechanically ventilated patients - comparison of two sampling methods

Exhaled breath analysis is a promising new diagnostic tool, but currently no standardised method for sampling is available in mechanically ventilated patients. We compared two breath sampling methods, first using an artificial ventilator circuit, then in "real life" in mechanically ventilated patients on the intensive care unit. In the laboratory circuit, a 24-component synthetic-breath volatile organic compound (VOC) mixture was injected into the system as air was sampled: (A) through a port on the exhalation limb of the circuit and (B) through a closed endo-bronchial suction catheter. Sorbent tubes were used to collect samples for analysis by thermal desorption-gas chromatography-mass spectrometry. Realistic mechanical ventilation rates and breath pressure-volume loops were established and method detection limits (MDLs) were calculated for all VOCs. Higher yields of VOCs were retrieved using the closed suction catheter; however, for several VOCs MDLs were compromised due to the background signal associated with plastic and rubber components in the catheters. Different brands of suction catheter were compared. Exhaled VOC data from 40 patient samples collected at two sites were then used to calculate the proportion of data analysed above the MDL. The relative performance of the two methods differed depending on the VOC under study and both methods showed sensitivity towards different exhaled VOCs. Furthermore, method performance differed depending on recruitment site, as the centres were equipped with different brands of respiratory equipment, an important consideration for the design of multicentre studies investigating exhaled VOCs in mechanically ventilated patients.

Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study

BACKGROUND

Early diagnosis of coronavirus disease 2019 (COVID-19) is of the utmost importance but remains challenging. The objective of the current study was to characterize exhaled breath from mechanically ventilated adults with COVID-19.

METHODS

In this prospective observational study, we used real-time, online, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of expired air from adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS).

FINDINGS

Between March 25th and June 25th, 2020, we included 40 patients with ARDS, of whom 28 had proven COVID-19. In a multivariate analysis, we identified a characteristic breathprint for COVID-19. We could differentiate between COVID-19 and non-COVID-19 ARDS with accuracy of 93% (sensitivity: 90%, specificity: 94%, area under the receiver operating characteristic curve: 0·94-0·98, after cross-validation). The four most prominent volatile compounds in COVID-19 patients were methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal.

INTERPRETATION

The real-time, non-invasive detection of methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal in exhaled breath may identify ARDS patients with COVID-19.

FUNDING

The study was funded by Agence Nationale de la Recherche (SoftwAiR, ANR-18-CE45-0017 and RHU4 RECORDS, Programme d'Investissements d'Avenir, ANR-18-RHUS-0004), Région Île de France (SESAME 2016), and Fondation Foch.

Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition

In vitro cultivation of staphylococci is fundamental to both clinical and research microbiology, but few studies, to-date, have investigated how the differences in rich media can influence the volatilome of cultivated bacteria. The objective of this study was to determine the influence of rich media composition on the chemical characteristics of the volatilomes of Staphylococcus aureus and Staphylococcus epidermidis. S. aureus (ATCC 12600) and S. epidermidis (ATCC 12228) were cultured in triplicate in four rich complex media (brain heart infusion (BHI), lysogeny broth (LB), Mueller Hinton broth (MHB), and tryptic soy broth (TSB)), and the volatile metabolites produced by each culture were analyzed using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography—time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). When comparing the chemical compositions of the staph volatilomes by the presence versus absence of volatiles produced in each medium, we observed few differences. However, when the relative abundances of volatiles were included in the analyses, we observed that culturing staph in media containing free glucose (BHI and TSB) resulted in volatilomes dominated by acids and esters (67%). The low-glucose media (LB and MHB) produced ketones in greatest relative abundances, but the volatilome compositions in these two media were highly dissimilar. We conclude that the staphylococcal volatilome is strongly influenced by the nutritional composition of the growth medium, especially the availability of free glucose, which is much more evident when the relative abundances of the volatiles are analyzed, compared to the presence versus absence.

Microbiota-derived metabolites as diagnostic markers for respiratory fungal infections

An emerging body of evidence has highlighted the significant role of the pulmonary microbiota during respiratory infections. The individual microbiome is nowadays recognized to supervise the outcome of the host-pathogen interaction by orchestrating mechanisms of immune regulation, inflammation, metabolism, and other physiological processes. A shift in the normal flora of the respiratory tract is associated with several lung inflammatory disorders including asthma, chronic obstructive pulmonary disease, or cystic fibrosis. These diseases are characterized by a lung microenvironment that becomes permissive to infections caused by the opportunistic fungal pathogen Aspergillus fumigatus. Although the role of the lung microbiota in the pathophysiology of respiratory fungal diseases remains elusive, microbiota-derived components have been proposed as important biomarkers to be considered in the diagnosis of these severe infections. Here, we review this emerging area of research and discuss the potential of microbiota-derived products in the diagnosis of respiratory fungal diseases.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

A Filter Supported Surface-Enhanced Raman Scattering "Nose" for Point-of-Care Monitoring of Gaseous Metabolites of Bacteria

This work designs a convenient method for fabrication of surface-enhanced Raman scattering (SERS) devices by loading gold nanostars (AuNSs) on a flat filter support with vacuum filtration. The dense accumulation of AuNSs results in a strong sensitization to SERS signal and shows sensitive response to gaseous metabolites of bacteria, which produces a SERS "nose" for rapid point-of-care monitoring of these metabolites. The "nose" shows good reproducibility and stability and can be used for SERS quantitation of a gaseous target with Raman signal. The impressive performance of the proposed SERS "nose" for detecting gaseous metabolites of common foodborne bacteria like Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa from inoculated samples demonstrates its much higher sensitivity than that of human sense and application in distinguishing spoiled food at an early stage and real-time tracing of food spoilage degree. The strong point-of-care testing ability of the designed SERS "nose" and the miniaturization of whole equipment extend greatly the analytical application of SERS technology in food safety and public health.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Background

Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.

Methods

We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy.

Results

A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement.

Conclusions

There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

A pathogen-derived metabolite induces microglial activation via odorant receptors

Microglia (MG), the principal neuroimmune sentinels in the brain, continuously sense changes in their environment and respond to invading pathogens, toxins, and cellular debris, thereby affecting neuroinflammation. Microbial pathogens produce small metabolites that influence neuroinflammation, but the molecular mechanisms that determine whether pathogen-derived small metabolites affect microglial activation of neuroinflammation remain to be elucidated. We hypothesized that odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, are involved in microglial activation by pathogen-derived small metabolites. We found that MG express high levels of two mouse ORs, Olfr110 and Olfr111, which recognize a pathogenic metabolite, 2-pentylfuran, secreted by Streptococcus pneumoniae. These interactions activate MG to engage in chemotaxis, cytokine production, phagocytosis, and reactive oxygen species generation. These effects were mediated through the G_αs -cyclic adenosine monophosphate-protein kinase A-extracellular signal-regulated kinase and G_βγ -phospholipase C-Ca²⁺ pathways. Taken together, our results reveal a novel interplay between the pathogen-derived metabolite and ORs, which has major implications for our understanding of microglial activation by pathogen recognition. DATABASE: Model data are available in the PMDB database under the accession number PM0082389.

On-line profiling of volatile compounds produced in vitro by pathogenic oral bacteria

Infections by oral pathogens are one of the most common health problems worldwide. Due to the intimate connection between exhaled breath and the oral cavity, breath analysis could potentially be used to diagnose these infections. However, little is known about the volatile emissions of important oral pathogens that are connected with gingivitis and periodontitis. In this study, we have performed in vitro headspace measurements on four important oral pathogens (P. gingivalis, T. forsythia, P. intermedia and P. nigrescens) using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). Some of the most abundant compounds produced by the bacteria include hydrogen sulphide, methanethiol, acetone, dimethylsulphide, isoprene, cyclopentanone and indole as tentatively assigned from the mass spectra. Several other abundant mass signals were recorded but the assignment of these is less certain. Some of the bacterial species can be separated from each other by the emitted volatile fingerprints. The results of this study can be used in potential development of a diagnostic breath test for oral infections. In addition, as several of the measured compounds are known to be toxic, the results point to an intriguing possibility of studying the connection between the bacterial virulence and the emitted volatile compounds.

Volatile scents of influenza A and S. pyogenes (co-)infected cells

Influenza A is a serious pathogen itself, but often leads to dangerous co-infections in combination with bacterial species such as Streptococcus pyogenes. In comparison to classical biochemical methods, analysis of volatile organic compounds (VOCs) in headspace above cultures can enable destruction free monitoring of metabolic processes in vitro. Thus, volatile biomarkers emitted from biological cell cultures and pathogens could serve for monitoring of infection processes in vitro. In this study we analysed VOCs from headspace above (co)-infected human cells by using a customized sampling system. For investigating the influenza A mono-infection and the viral-bacterial co-infection in vitro, we analysed VOCs from Detroit cells inoculated with influenza A virus and S. pyogenes by means of needle-trap micro-extraction (NTME) and gas chromatography mass spectrometry (GC-MS). Besides the determination of microbiological data such as cell count, cytokines, virus load and bacterial load, emissions from cell medium, uninfected cells and bacteria mono-infected cells were analysed. Significant differences in emitted VOC concentrations were identified between non-infected and infected cells. After inoculation with S. pyogenes, bacterial infection was mirrored by increased emissions of acetaldehyde and propanal. N-propyl acetate was linked to viral infection. Non-destructive monitoring of infections by means of VOC analysis may open a new window for infection research and clinical applications. VOC analysis could enable early recognition of pathogen presence and in-depth understanding of their etiopathology.

Emerging strategies for the noninvasive diagnosis of nosocomial pneumonia

Introduction: Hospital-acquired pneumonia is a common and therapeutically challenging diagnosis that can lead to severe sepsis, critical illness, and respiratory failure. In this review, we focus on efforts to enhance microbiological diagnosis of hospital-acquired pneumonia, including ventilator-associated pneumonia.

Areas covered: A systematic literature review was conducted by searching Medline from inception to December 2018, including hand-searching of the reference lists for additional studies. The search strategy comprised the following common search terms: hospital pneumonia OR nosocomial pneumonia OR noninvasive OR molecular diagnostic tests (OR point-of-care systems OR VOC [i.e. volatile organic compounds]) OR rapid (or simple or quick test), including brand names for the most common commercial tests.

Expert opinion: In recent years, the microbiological diagnosis of respiratory pathogens has improved significantly by the development and implementation of molecular diagnostic tests for pneumonia. Real-time polymerase chain reaction, hybridization, and mass spectrometry-based platforms dominate the scene, with microarray-based assays, multiplex polymerase chain reaction, and MALDI-TOF mass spectrometry capable of detecting the determinants of antimicrobial resistance (mainly β-lactamase genes). Introducing these assays into routine clinical practice for rapid identification of the causative microbes and their resistance patterns could transform the care of pneumonia, improving antimicrobial selection, de-escalation, and stewardship.

Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept study

Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae ( SP) or Pseudomonas aeruginosa ( PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography-mass-spectrometry (TD-GC-MS) and selected ion flow tube-mass spectrometry (SIFT-MS) of exhaled breath. Male adult rats ( n = 50) received an intratracheal inoculation of 1) 200 µl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TD-GC-MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73–0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94–1) and CCR of 99.9% for SP versus PA, 0.92 (0.83–1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92–1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens.

Soluble urokinase plasminogen activator receptor for the prediction of ventilator-associated pneumonia

Introduction

Diagnosing ventilator-associated pneumonia (VAP) remains challenging. Soluble urokinase plasminogen activator receptor (suPAR) has prognostic value in critically ill patients with systemic infection. We hypothesised that plasma suPAR levels accurately predict development of VAP.

Methods

This observational, multicentre, prospective cohort study compared patients at risk for VAP with a control group. Plasma and tracheal aspirate samples were collected. Plasma suPAR levels were measured on the day of diagnosis and 3 days before diagnosis.

Results

The study included 24 VAP patients and 19 control patients. The suPAR concentration measured 3 days before diagnosis was significantly increased in VAP patients versus matched samples of control patients (area under the receiver operating characteristic curve (AUC) 0.68, 95% CI 0.52–1.00; p=0.04). Similar results were found on the day of diagnosis (AUC 0.77, 95% CI 0.6–0.93; p=0.01). Plasma suPAR was significantly higher in deceased patients (AUC 0.79, 95% CI 0.57–1.00; p<0.001). Combining suPAR with the Clinical Pulmonary Infection Score, C-reactive protein and/or procalcitonin led to a significantly increased discriminative accuracy for predicting VAP and an increased specificity.

Conclusions

suPAR can be used to diagnose VAP with a fair diagnostic accuracy and has a moderate prognostic accuracy to be used in critically ill intensive care unit patients. Its performance improves when added to other clinically available biomarkers (C-reactive protein and procalcitonin) or scoring systems (Clinical Pulmonary Infection Score and Sepsis-related Organ Failure Assessment).

suPAR can be used to diagnose VAP with a fair diagnostic accuracy and has a moderate prognostic accuracy to be used in critically ill ICU patientshttp://ow.ly/ubmf30nIgZO

A comprehensive look into the volatile exometabolome of enteroxic and non-enterotoxic Staphylococcus aureus strains

Staphylococcal food poisoning is a disease that originates significant health and economic losses and is caused by Staphylococcus aureus strains able to produce enterotoxins. The aim of this work is to go further on the study of the volatile exometabolome of S. aureus using an advanced gas chromatographic technique. Enterotoxic and non-enterotoxic strains were assessed. The volatile exometabolome profile comprised 240 volatiles belonging to ten chemical families. This volatiles were mainly by-products of branched-chain amino acids and methionine degradation, pyruvate metabolism, diacetyl pathway, oxidative stress and carotenoid cleavage. Metabolites released by the first two pathways were produced in higher contents by the enterotoxic strains. This study add further insights to S. aureus volatile exometabolome, and also shows that by applying it, it is possible to distinguish strains of S. aureus by the number of produced enterotoxins, which is especially important from the food safety point of view.

Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

Volatile fingerprinting of Pseudomonas aeruginosa and respiratory syncytial virus infection in an in vitro cystic fibrosis co-infection model

Volatile molecules in exhaled breath represent potential biomarkers in the setting of infectious diseases, particularly those affecting the respiratory tract. In particular, Pseudomonas aeruginosa is a critically important respiratory pathogen in specific subsets of the population, such as those with cystic fibrosis (CF). Infections caused by P. aeruginosa can be particularly problematic when co-infection with respiratory syncytial virus (RSV) occurs, as this is correlated with the establishment of chronic P. aeruginosa infection. In the present study, we evaluate the volatile metabolites produced by P. aeruginosa (PAO1)-infected, RSV-infected, co-infected, or uninfected CF bronchial epithelial (CFBE) cells, in vitro. We identified a volatile metabolic signature that could discriminate between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE with an area under the receiver operating characteristic curve (AUROC) of 0.850, using the machine learning algorithm random forest (RF). Although we could not discriminate between RSV-infected and non-RSV-infected CFBE (AUROC = 0.431), we note that sample classification probabilities for RSV-infected cell, generated using RF, were between those of uninfected CFBE and P. aeruginosa-infected CFBE, suggesting that RSV infection may result in a volatile metabolic profile that shares attributes with both of these groups. To more precisely elucidate the biological origins of the volatile metabolites that were discriminatory between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE, we measured the volatile metabolites produced by P. aeruginosa grown in the absence of CFBE. Our findings suggest that the discriminatory metabolites produced likely result from the interaction of P. aeruginosa with the CFBE cells, rather than the metabolism of media components by the bacterium. Taken together, our findings support the notion that P. aeruginosa interacting with CFBE yields a particular volatile metabolic signature. Such a signature may have clinical utility in the monitoring of individuals with CF.

Pulmonary infections in critical/intensive care - rapid diagnosis and optimizing antimicrobial usage

PURPOSE OF REVIEW

Diagnosis of pulmonary infection, including hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) in the critically ill patient remains a common and therapeutically challenging diagnosis with significant attributable morbidity, mortality, and cost. Current clinical approaches to surveillance, early detection and, conventional culture-based microbiology are inadequate for optimal targeted antibiotic treatment and stewardship. Efforts to enhance diagnosis of HAP and VAP and the impact of these novel approaches on rational antimicrobial selection and stewardship are the focus of recent studies reviewed here.

RECENT FINDINGS

Recent consensus guidelines for diagnosis and management of HAP and VAP are relatively silent on the potential role of novel rapid microbiological techniques and reply heavily on conventional culture strategies of noninvasively obtained (including endotracheal aspirate samples). Novel rapid microbiological diagnostics, including nucleic acid amplification, mass spectrometry, and fluorescence microscopy-based technologies are promising approaches for the future. Exhaled breath biomarkers, including measurement of VOC represent a future approach.

SUMMARY

Further validation of novel diagnostic technology platforms will be required to evaluate their utility for enhancing diagnosis and guiding treatment of pulmonary infections in the critically ill. However, the integration of novel diagnostics for rapid microbial identification, resistance phenotyping, and antibiotic sensitivity testing into usual care practice could significantly transform the care of patients and potentially inform improved targeted antimicrobial selection, de-escalation, and stewardship.

Towards the use of breath for detecting mycobacterial infection: a case study in a murine model

In the present research, the potential of breath analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was investigated for the discrimination between healthy and infected mice. A pilot study employing a total of 16 animals was used to develop a method for breath analysis in a murine model for studying Mycobacterium tuberculosis complex (MTBC) using the M. bovis bacillus Calmette-Guérin. Breath was collected in Tedlar bags and concentrated onto thermal desorption tubes for subsequent analysis by GC×GC-MS. Immunological test and bacterial cell count in bronchoalveolar lavage fluid and mice lung homogenate confirmed the presence of bacteria in the infected group. From the GC×GC-MS analysis, 23 molecules were found to mainly drive the separation between control and infected mice and their tentative identification is provided.This study shows that the overall used methodology is able to differentiate breath between healthy and infected animals, and the information herein can be used to further develop the mouse breath model to study MTBC pathogenesis, evaluate pre-clinical drug regimen efficacy, and to further develop the concept of breath-based diagnostics.